Four key contributors to the Analytics Chasm were mentioned earlier this week. Here we elaborate on “Why current organization designs impede business effectiveness despite the promise of Big Data and Analytics?”
Big Data for businesses is akin to high octane fuel for engines: a promise of high performance.
Discovery of a “power fuel” which could revolutionize engine performance cannot be administered without considering design changes that the engine need to go through. This metaphor could well represent the current organization (engine) design and the promise of Big Data (power fuel).
Big Data offers several exciting possibilities – extensive availability of business data, predictive analysis of outcomes, rich data which reveal very granular operational contexts and so on. Thus, the promise of Big Data is staggering for the business to reinvent itself and be nifty while at it. Big Data (& ensuing Analysis) encapsulate multiple facets of business drivers which the organization need to act on to keep itself competitive and relevant to all its stakeholders.
Question is, does the current organizational designs & management models enable this “power fuel” to permeate across organizational structures, interfaces & processes to deliver the promise.
Organization decisions have a hierarchical flow. Big Data doesn’t have any.
Organizations continue to be pyramidal structures where flow of decisional authority is still (in most cases) unidirectional – from top to the lower layers of the pyramid. Within the authorization matrix stipulated by this hierarchical flow; there are no design considerations for infusing Big Data led decisions & actions. Big Data does not follow any hierarchical direction or pattern. By its’ very nature it will throw up information & insights which are not obvious & even counter-intuitive to the established wisdom. Despite these powerful revelations, variations from organizational processes still need to be “send up” for approval before actions. This could heavily impede business velocity.
How Big Data promises to revolutionize but is limited in its delivery due to the current organizational design arrangement could be explored through some examples.
- A local manager’s effort to change regional sales tactics based on real time social data will be thwarted since it may need deviations from established guidance norms. Organizations are not structurally or functionally evolved to consider these variations quickly. People farthest from this data by virtue of their authority will continue to stifle these actions.
- How many infrastructure and project management companies are wired internally to listen and include public domain data alerts on scarcity of ground water while executing ‘sustainable’ long term projects?
- The food industry business which tracks obesity growth rates need to instruct their manufacturing units to progressively reduce sugar content in their products. Progressive reduction of sugar in the products (so as not to lose current market share) while developing a healthier taste alternative will be hard to come by since turf war, authority and “gut feel” will kill what the data conveys.
- Survey mechanisms for designing employee engagement programs still come from a few “experts.” Company intranet discussions which has insights on the sentiments of the employees which is a guiding beacon for the organization often gets ignored.
Organization designs are from the 20th century. Big Data is from the 21st century.
In all the examples above companies may have invested or have access to Big Data Analysis. Their current organizational design are NOT wired to make directional changes based on the “surprises”, “anomalies” and “variations” that Big Data throws at them. The decision process continues to be limited to certain parts of the organization by virtue of their current functional/structural designs. Peter Drucker’s comment (though made before the era of Big Data) amply represents this design flaw “most discussions of decision making assume, that only senior executives make decisions or that only senior executives’ decisions matter. This is a dangerous mistake.”
What is discussed here is only one of the many strands of organizational design – hierarchical decision models and how it limits the business effectiveness which Big Data Analysis can provide. Organizational designs and management models which have been followed without major changes since the beginning of 20th century, relies heavily on upper echelons of organizational hierarchy to take decisions.
Though it can be argued that organizational design is archaic & unevolved even to handle “small data” decisions; Big Data further exposes the design short comings since businesses are investing heavily in Big Data with the ardent hope that that it will help to adapt and reinvent themselves even faster.
Though it is not within the scope of this discussion and beyond this author’s ken to define alternative organizational designs, but some techniques could be pointed out that will help to leverage Big Data more effectively within the current tenets of structural and functional design of an organization.
- Democratize Big Data. Promote tools and techniques for many to understand what the Big Data conveys. More eyes “see” the data, more the chance of insights being understood by the organizations.
- Wisdom of a few need to listen to the wisdom of the many. Do not use Big Data to validate existing decisions & operating procedures defined by a “few” in authority. Instead actively seek deviations which could even question established decision practices. While allocating resources based on Big Data Analysis; the decision makers need to promote differing viewpoints which will come from distributed teams who are functionally or geographically separated from the “think tanks.”
- Technology upgrade is not the end but a means. Before embarking on Big Data journey at least identify, if not choose, some key areas where Big Data could bring large benefits. Complete the Big Data journey with key business results for these areas (not mere technology upgrades) and ride on the momentum to apply the learning to roll it out for the entire organization.
Some of the above techniques will help reduce the effects of “Analytics Chasms.” Management innovation could eventually prescribe a paradigm shift in organizational design to cope with modern business realities – till then, we will use techniques to tinker with organizational process and designs to maximize Big Data promises.